Underwater acoustic sensor networks (UASNs) play a pivotal role in various civil and military fields. However, due to their open nature, they are susceptible to multiple security threats. As such, developing robust an...
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Underwater acoustic sensor networks (UASNs) play a pivotal role in various civil and military fields. However, due to their open nature, they are susceptible to multiple security threats. As such, developing robust and reliable security strategies is essential to ensure the normal operation of UASNs. This paper proposes a Q-learning-based trust model (QLTM) for UASNs. To detect hostile nodes, each underwater sensor node is required to collect trust evidence –namely energy trust evidence, data trust evidence, and communication trust evidence–through communication and interaction with its neighboring nodes. After gathering the trust evidence, QLTM presents a distributed Q-learning-based trust management model that adapts to dynamic underwater environments. It continuously updates the trust parameters based on ongoing interactions between the agent and the environment. The Q-learning-based trust management model includes a state set with three states: trust, distrust, and uncertain. Additionally, the reward function is calculated according to the gathered trust evidence, and the weight of each trust evidence is determined such that evidence with a lower value carries more weight, thus having a greater effect on the generated reward. Experimental results demonstrate the effectiveness of QLTM compared to other trust mechanisms, so that QLTM improves the detection accuracy rate by 5.04%. However, when the attack mode changes in the network, QLTM performs approximately 4.29% worse than TUMRL in detecting malicious nodes. On the other hand, QLTM reduces the false alarm rate by about 7.39% and increases energy efficiency by approximately 4.26%.
Automatic surgical workflow recognition is an essential step in developing context-aware computer-assisted surgical systems. Video recordings of surgeries are becoming widely accessible, as the operational field view ...
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Automatic surgical workflow recognition is an essential step in developing context-aware computer-assisted surgical systems. Video recordings of surgeries are becoming widely accessible, as the operational field view is captured during laparoscopic surgeries. Head and ceiling mounted cameras are also increasingly being used to record videos in open surgeries. This makes videos a common choice in surgical workflow recognition. Additional modalities, such as kinematic data captured during robot-assisted surgeries, could also increase the workflow recognition rate. The "MIcro-Surgical Anastomose Workflow recognition on training sessions" (MISAW) challenge provided a data set of 27 sequences of micro-surgical anastomosis on artificial blood vessels. This data set was composed of videos, kinematics, and workflow annotations. The latter described the sequences at three different granularity levels: phase, step, and activity. The participants were given the option to use kinematic data and videos to develop workflow recognition models. Four tasks were proposed to the participants: three of them were related to the recognition of surgical workflow at three different granularity levels, while the last one addressed the recognition of all granularity levels in the same model. One ranking was made for each task. Additionally, to evaluate whether the recognition of several granularity levels could improve the recognition rate of each individual granularity, multi-granularity recognition models were also ranked with the uni-granularity ones. We used the average application-dependent balanced accuracy (AD-Accuracy) as the evaluation metric. This takes unbalanced classes into account and it is more clinically relevant than a frame-by-frame score. Six teams, including a non-competing team, participated in at least one task. All models employed deep learning models, such as convolutional neural networks (CNN), recurrent neural networks (RNN), or a combination of both. The best model
Coarctation of the aorta (CoA) is a congenital tightening of the proximal descending aorta. Flow quantification can be immensely valuable for an early and accurate diagnosis. However, there is a lack of appropriate di...
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In recent years, quantitative approaches based on mathematical theories and ICT tools, known under the terms of digital, computational, and virtual archaeology, are more and more involved in the traditional archaeolog...
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Background: In 2024, WHO included effective refractive error coverage (eREC) into the results framework of the 14th General Programme of Work, which sets a road map for global health and guides WHO's work between ...
Background: In 2024, WHO included effective refractive error coverage (eREC) into the results framework of the 14th General Programme of Work, which sets a road map for global health and guides WHO's work between 2025 and 2028. eREC is a measure of both the availability and quality of refractive correction in a population. This study aimed to model global and regional estimates of eREC as of 2023 and evaluate progress towards the WHO global target of a 40 percentage-point absolute increase in eREC by 2030. Methods: For this systematic review and meta-analysis, the vision Loss Expert Group analysed data from 237 population-based eye surveys conducted in 76 countries since 2000, comprising 815 273 participants, to calculate eREC (met need / met need + undermet need + unmet need]) and the relative quality gap between eREC and REC ([REC – eREC] / REC × 100, where REC = [met + undermet need] / [met need + undermet need + unmet need]). An expert elicitation process was used to choose covariates for a Bayesian logistic regression model used to estimate eREC by country–age–sex grouping among adults aged 50 years and older. Country–age–sex group estimates were aggregated to provide estimates according to Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) super-regions. Findings: Global eREC was estimated to be 65·8% (95% uncertainty interval [UI] 64·7–66·8) in 2023, 6 percentage points higher than in 2010 (eREC 59·8% [59·4–60·2]). There were marked differences in eREC between GBD super-regions in 2023, ranging from 84·0% (95% UI 83·0–85·0) in high-income countries to 28·3% (26·4–30·4) in sub-Saharan Africa. In all super-regions, eREC was lower in females than males, and decreased with increasing age among adults aged ≥50 years. Since 2000, the relative increase in eREC was 60·2% in sub-Saharan Africa, 45·7% in North Africa and the Middle East, 41·5% in southeast Asia, east Asia and Oceania, 40·3% in south Asia, 16·2% in Latin America and the Caribbean, 8·3% in
Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the ...
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Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of...
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The Extra Robotic Legs (XRL) system is a robotic augmentation worn by a human operator consisting of two articulated robot legs that walk with the operator and help bear a heavy backpack payload. It is desirable for t...
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ISBN:
(数字)9781728173955
ISBN:
(纸本)9781728173962
The Extra Robotic Legs (XRL) system is a robotic augmentation worn by a human operator consisting of two articulated robot legs that walk with the operator and help bear a heavy backpack payload. It is desirable for the Human-XRL quadruped system to walk with the rear legs lead the front by 25% of the gait period, minimizing the energy lost from foot impacts while maximizing balance stability. Unlike quadrupedal robots, the XRL cannot command the human's limbs to coordinate quadrupedal locomotion. Using a pair of Rimless Wheel models, it is shown that the systems coupled with a spring and damper converge to the desired 25% phase difference. A Poincaré return map was generated using numerical simulation to examine the convergence properties to different coupler design parameters, and initial conditions. The Dynamically Coupled Double Rimless Wheel system was physically realized with a spring and dashpot chosen from the theoretical results, and initial experiments indicate that the desired synchronization properties may be achieved within several steps using this set of passive components alone.
This paper focuses on the face detection problem of three popular animal categories that need control such as horses, cats and dogs. To be precise, a new Convolutional Neural Network for Animal Face Detection (CNNAFD)...
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INTRODUCTION: Understanding the neurometabolic changes associated with amyloid-β (Aβ) deposition is important for early Alzheimer's disease (AD) diagnosis, but their spatial relationships remained unexplored due...
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